Computer learning techniques have been implemented in computer systems, and effectively used in an analysis of images, to, for example, identify objects of interest to a user. Learning frameworks provide a method for computers to discover important characteristics or features of a selected object, such as, for example, a human face. In some known learning frameworks, the features used by the system are preselected by the user, and the framework learns the relative utility, useful ranges, or relationships between the features that can then be used by the computer system to identify the selected objects of interest that may appear in an image. In other known systems, a large set of features is evaluated by the learning framework, for identification of particular features that are important to an object identification task.
In real world environments, an object recognition system must be able to function under a wide variety of illumination conditions, including shadows, and distinguish among significant variations of object types. For example, a system set up to identify automobiles analyzes an image for features indicative of the structure of an automobile. However, an automobile depicted in the image can have characteristic features that vary in color, orientation, appear in shadow, or have features that cast a shadow, thereby altering the shape of the object as ascertained by the computer system. Thus, the learning framework must include considerable details not only on characteristic features per se, but also on how perception of each characteristic feature can be altered by varying illumination in a scene depicted in an image. One known approach to achieving an illumination and color invariant system is to use differential or frequency based features of grayscale imagery. However, the real world consequences of varying illumination adds a complexity to the image analysis that can affect the efficiency and accuracy of an object recognition system.